The present invention relates generally to an application that is employed to structure a collective decision making process. More specifically the present invention relates to a software-implemented structured decision engine that includes real time bias weighting in order to collect input from and provide assistance to a plurality of people involved in a decision making process.
The process of making a decision is an inevitable part of everyday life and the results produced by many decisions may have critical impact on the world around us. While decisions are made every day, some of them are made with great thought while others are made spontaneously. Virtually all of the decisions that we make, however, are within our realm of control and have an effect on the direction that our lives will take and furthermore may have an impact on the world around us. Therefore, for every decision making situation, it is important to make the most informed decision possible with all of the information available at that moment for that situation.
In many decision making situations, the decision rests solely with one actor or with a small group of actors that possess all of the available information in a single location. In these cases, the process is made relatively easy in that there are no unknown pieces of information and all of the actors can meet face-to-face and work through a collaborative discussion to arrive at a decision. However, there are an increasing number of decision making situations where there are a plurality of actors that are all remotely located from one another that must all collaborated to reach a consensus opinion. To facilitate decision making in such environments, there are a number of computer-implemented programs currently available for use in decision making situations in the context of network conferencing, such as teleconferencing, videoconferencing and the like. These programs extend traditional conferencing capabilities to the desktop computer where individuals can participate in conferences from their home or office. In addition, multi-user interaction can be advanced through collaboration management programs that enable data sharing across multiple hardware and software platforms.
Often the software platforms that are employed in such decision collaboration systems are based on belief networks or B-nets. A belief network is a representation of the probabilistic relationships that exist between that various different choices that are available as a solution set to a problem. In this regard, a distinct solution in a belief network can take on a set of values and are thus called variables. A belief network is expressed as an acyclic, directed graph, where the variables correspond to nodes and where the relationships between the nodes correspond to arcs.
When constructing a belief network the participants all provide the entire set of possible solutions that, in their estimation, are available to them for making a given decision. Once the comprehensive solution set is collected, each solution set is established as a node within the belief network. Then, to complete the belief network, an algorithm is employed to gather evidence (both supporting and detracting) and apply that evidence to each of the nodes in a manner that produces dependencies among the nodes and probability distributions that quantify the strengths of the dependencies. This data and the relational evidence collectively serve to create the underlying belief network. After the belief network has been constructed, the belief network becomes the engine for a decision-support system. A computer system then uses the belief network to perform probabilistic inference by determining the probabilities of variable states given observations, to determine the benefits of performing tests, and ultimately to recommend or render a decision.
The difficulty in the use of prior art belief networks in the context of collective decision making is that all of the evidence is equally weighted. In operation, as the users work through the decision making process each of the collaborators adds evidence into the belief network to either support or detract from any particular solution set. While in practice this process may on its face appear ideal, the difficulty is that many times collaborators will come to the process with a predetermined bias or a predetermined concept of their ultimate outcome. As a result in operation these system are generally driven more by egocentricities than by wholly objective evidence.
Therefore, there is a need for a method and system that allows a group of individuals or entities to act as a single decision-making unit in determining whether to implement a course of action. There is a further need for a method and system that can be implemented over a wide area computer network in a manner that allows a group of decision making entities to collaborate in real time in determining whether to implement a course of action. Finally there is still a further need for a method and system that can be implemented over a wide area computer network in a manner that allows a group of decision making entities to collaborate while also allowing each of the entities to be assigned a weighting factor and a bias factor that assists in evaluating their presentation of evidence in the proper context.